AI 中文总结
本文针对云量的量子模型构建影子模型，对比经典插值与量子傅里叶模型近似，发现其可缓解有限采样噪声，在Euro-Q-Exa系统上也观测到误差缓解效果。
AI 中文摘要
量子计算是一个快速发展的领域，相比传统计算具有诸多潜在优势。然而目前独立的量子应用仍较为稀缺，混合（量子-经典）计算成为必要，尤其在量子机器学习（QML）领域。由于当前量子计算硬件的局限性，以及高性能计算（HPC）与量子设备之间的耦合问题，将已训练的QML模型集成到经典应用中颇具挑战性。在此场景下，耦合所谓的QML模型影子会很有帮助，影子即模仿QML模型输入输出关系的经典模型，仅在训练阶段需要量子资源。本文考虑无显式训练或回归阶段的建设性影子过程，以避免QML模型冗余，并将其应用于先前开发的云量QML模型[1]，从而实现与气候模型的高效耦合。我们将经典插值方法与量子傅里叶模型的近似（即电路的部分傅里叶级数表示）进行比较。文献[1]中的编码策略允许使用离散傅里叶变换在经典层面高效重构电路，截断部分傅里叶级数可进一步减小影子模型的规模。两种方法在特定条件下均能缓解有限采样噪声，这为在硬件可用性受限的时期之外使用影子模型提供了依据。此外，我们在基于IQM Radiance超导量子比特系统的Euro-Q-Exa量子系统上计算了影子模型，也观察到了误差缓解效果，尽管目前仍难以将其与系统校准相关的误差区分开来。
英文摘要
Quantum computing is a quickly growing field that is promising various advantages compared to conventional computing. However, currently stand-alone quantum applications are scarce and hybrid (quantum-classical) computing is needed, especially in quantum machine learning (QML). Due to current limitations of quantum computing hardware and the coupling between HPC and quantum devices, integrating a trained (QML) model in classical applications is challenging. In this case, it is helpful to couple so-called shadows of the QML model instead, i.e., classical models that imitate the input-output relations of QML models such that quantum resources are only needed during the training stage. Here we consider constructive shadowing processes without an explicit training or regression stage to avoid rendering the QML model redundant, and apply them to a previously developed QML model for cloud cover [1] to allow for an efficient coupling to a climate model. We compare classical interpolation methods to an approximation of the quantum Fourier model, the representation of the circuit as a partial Fourier series. The encoding strategy in [1] allows the use of the discrete Fourier transform to efficiently reconstruct the circuits classically. Truncating the partial Fourier series further reduces the size of the shadow models. Both methods have the effect of mitigating finite sampling noise under certain conditions, which yields a motivation to use shadow models also beyond the era of limited hardware availability. Further, we compute the shadow models on the quantum system Euro-Q-Exa, based on the IQM Radiance system with superconducting qubits, where error mitigating effects can also be observed, albeit it is still difficult to distinguish them from errors connected to the calibration of the system.
